Genetic variations at loci involved in the immune response are risk factors for hepatocellular carcinoma.

UNLABELLED: Primary liver cancer is the third most common cause of cancer-related death worldwide, with a rising incidence in Western countries. Little is known about the genetic etiology of this disease. To identify genetic factors associated with hepatocellular carcinoma (HCC) and liver cirrhosis (LC), we conducted a comprehensive, genome-wide variation analysis in a population of unrelated Asian individuals. Copy number variation (CNV) and single nucleotide polymorphisms (SNPs) were assayed in peripheral blood with the high-density Affymetrix SNP6.0 microarray platform. We used a two-stage discovery and replication design to control for overfitting and to validate observed results. We identified a strong association with CNV at the T-cell receptor gamma and alpha loci (P < 1 × 10(-15)) in HCC cases when contrasted with controls. This variation appears to be somatic in origin, reflecting differences between T-cell receptor processing in lymphocytes from individuals with liver disease and healthy individuals that is not attributable to chronic hepatitis virus infection. Analysis of constitutional variation identified three susceptibility loci including the class II MHC complex, whose protein products present antigen to T-cell receptors and mediate immune surveillance. Statistical analysis of biologic networks identified variation in the "antigen presentation and processing" pathway as being highly significantly associated with HCC (P = 1 × 10(-11)). SNP analysis identified two variants whose allele frequencies differ significantly between HCC and LC. One of these (P = 1.74 × 10(-12)) lies in the PTEN homolog TPTE2. CONCLUSION: Combined analysis of CNV, individual SNPs, and pathways suggest that HCC susceptibility is mediated by germline factors affecting the immune response and differences in T-cell receptor processing.

Rrp1b, a new candidate susceptibility gene for breast cancer progression and metastasis.

A novel candidate metastasis modifier, ribosomal RNA processing 1 homolog B (Rrp1b), was identified through two independent approaches. First, yeast two-hybrid, immunoprecipitation, and functional assays demonstrated a physical and functional interaction between Rrp1b and the previous identified metastasis modifier Sipa1. In parallel, using mouse and human metastasis gene expression data it was observed that extracellular matrix (ECM) genes are common components of metastasis predictive signatures, suggesting that ECM genes are either important markers or causal factors in metastasis. To investigate the relationship between ECM genes and poor prognosis in breast cancer, expression quantitative trait locus analysis of polyoma middle-T transgene-induced mammary tumor was performed. ECM gene expression was found to be consistently associated with Rrp1b expression. In vitro expression of Rrp1b significantly altered ECM gene expression, tumor growth, and dissemination in metastasis assays. Furthermore, a gene signature induced by ectopic expression of Rrp1b in tumor cells predicted survival in a human breast cancer gene expression dataset. Finally, constitutional polymorphism within RRP1B was found to be significantly associated with tumor progression in two independent breast cancer cohorts. These data suggest that RRP1B may be a novel susceptibility gene for breast cancer progression and metastasis.

A high-resolution multistrain haplotype analysis of laboratory mouse genome reveals three distinctive genetic variation patterns.

Understanding of the structure and the origin of genetic variation patterns in the laboratory inbred mouse provides insight into the utility of the mouse model for studying human complex diseases and strategies for disease gene mapping. In order to address this issue, we have constructed a multistrain, high-resolution haplotype map for the 99-Mb mouse Chromosome 16 using approximately 70,000 single nucleotide polymorphism (SNP) markers derived from whole-genome shotgun sequencing of five laboratory inbred strains. We discovered that large polymorphic blocks (i.e., regions where only two haplotypes, thus one SNP conformation, are found in the five strains), large monomorphic blocks (i.e., regions where the five strains share the same haplotype), and fragmented blocks (i.e., regions of greater complexity not resembling at all the first two categories) span 50%, 18%, and 32% of the chromosome, respectively. The haplotype map has 98% accuracy in predicting mouse genotypes in two other studies. Its predictions are also confirmed by experimental results obtained from resequencing of 40-kb genomic sequences at 21 distinct genomic loci in 13 laboratory inbred strains and 12 wild-derived strains. We demonstrate that historic recombination, intra-subspecies variations and inter-subspecies variations have all contributed to the formation of the three distinctive genetic signatures. The results suggest that the controlled complexity of the laboratory inbred strains may provide a means for uncovering the biological factors that have shaped genetic variation patterns.

Genomewide distribution of high-frequency, completely mismatching SNP haplotype pairs observed to be common across human populations.

Knowledge of human haplotype structure has important implications for strategies of disease-gene mapping and for understanding human evolutionary history. Many attributes of SNPs and haplotypes appear to exhibit highly nonrandom behavior, suggesting past operation of selection or other nonneutral forces. We report the exceptional abundance of a particular haplotype pattern in which two high-frequency haplotypes have different alleles at every SNP site (hence the name "yin yang haplotypes"). Analysis of common haplotypes in 62 random genomic loci and 85 gene coding regions in humans shows that the proportion of the genome spanned by yin yang haplotypes is 75%-85%. Population data of 28 genomic loci in Drosophila melanogaster reveal a similar pattern. The high recurrence (>/=85%) of these haplotype patterns in four distinct human populations suggests that the yin yang haplotypes are likely to predate the African diaspora. The pattern initially appeared to suggest deep population splitting or maintenance of ancient lineages by selection; however, coalescent simulation reveals that the yin yang phenomenon can be explained by strictly neutral evolution in a well-mixed population.

HapScope: a software system for automated and visual analysis of functionally annotated haplotypes.

We have developed a software analysis package, HapScope, which includes a comprehensive analysis pipeline and a sophisticated visualization tool for analyzing functionally annotated haplotypes. The HapScope analysis pipeline supports: (i) computational haplotype construction with an expectation-maximization or Bayesian statistical algorithm; (ii) SNP classification by protein coding change, homology to model organisms or putative regulatory regions; and (iii) minimum SNP subset selection by either a Brute Force Algorithm or a Greedy Partition Algorithm. The HapScope viewer displays genomic structure with haplotype information in an integrated environment, providing eight alternative views for assessing genetic and functional correlation. It has a user-friendly interface for: (i) haplotype block visualization; (ii) SNP subset selection; (iii) haplotype consolidation with subset SNP markers; (iv) incorporation of both experimentally determined haplotypes and computational results; and (v) data export for additional analysis. Comparison of haplotypes constructed by the statistical algorithms with those determined experimentally shows variation in haplotype prediction accuracies in genomic regions with different levels of nucleotide diversity. We have applied HapScope in analyzing haplotypes for candidate genes and genomic regions with extensive SNP and genotype data. We envision that the systematic approach of integrating functional genomic analysis with population haplotypes, supported by HapScope, will greatly facilitate current genetic disease research.